Читать книгу A Time Traveller's Guide to Our Next Ten Years - Frans Cronje - Страница 6

THE ART OF TRAVELLING IN TIME

The idea that we can describe the world in 2024 suggests that we can travel in time. In this chapter, we explore whether such a feat is at all possible, and, if so, what our time machine should look like, and how it should be piloted.

Academics and other analysts devote much of their time to identifying and explaining changes within countries. These diagnoses mostly deal with the past and the present, and draw on a formidable arsenal of established theory in the process. However, analysts are also often asked to comment on how the countries and economies they study may change in the future. While many try to respond, they often do so hesitantly, and without the benefit of established theories and methods similar to those for dealing with the past and the present. As a result, political scientists have a poor track record of anticipating shifts in political systems.

In fact, many of the most significant political and economic developments during the past 50 years were poorly predicted, or not predicted at all. In 1966, the American scenario planner Gill Ringland recalls, 27 leading American scientists were asked to predict what the world would need and want over the next 20 years.[1] Almost all of the 335 forecasts they generated proved to be entirely incorrect, mostly because they gave too much importance to state-driven megaprojects, which soon began to decline.

In an influential article published in 2003, the scenario planners Peter Kennedy and Charles Thomas (of the Futures Strategy Group in Glastonbury in Connecticut) cited the shock arrivals of the dot-com crisis and 9/11 terror attacks as examples of the failure of conventional political and economic forecasting.[2] Moreover, they warned that a lack of methods for dealing with future uncertainty would lead to more ‘future shocks’. Just six years later, and despite all the resources poured into political and economic forecasting, the 2009 global financial crisis took almost everyone by surprise, even though the diagnosis after the fact was relatively straightforward.

Peter Schwartz, former head of the renowned Shell scenario planning team, cites the example of a scenario called ‘The Greening of Russia’, which Royal Dutch Shell developed in 1983.[3] It held that, should Mikhail Gorbachev rise to power, this could lead to significant political and economic reforms in Russia. However, almost every Soviet expert presented with the scenario said it was entirely implausible. Even the Central Intelligence Agency (CIA) in the United States responded by saying, ‘You really don’t know what you’re talking about.’ Of course, in the end Shell was proved right and the CIA forecasters were proved wrong.

More recently the dramatic political events in Tunisia, Egypt, and Libya again took many scholars, diplomats, and journalists by surprise. During subsequent briefings to officials of the United States Department of State in Johannesburg and Washington, I made a point of asking whether the uprisings had been on their long-term political radar, to which the answer was ‘no’. I asked the same question during a meeting with representatives of the Israeli government in Jerusalem, and got the same answer.

It is extraordinary that two advanced countries with highly sophisticated intelligence services and enormous interests in the stability of the Middle East and North Africa did not know that the entire region was on the verge of fundamental change. Intelligence agencies, think-tanks and universities in those two countries invest hundreds of millions of dollars a year in research on the Middle East and North Africa, but failed to provide their governments with direct advance warning of these momentous events, which have had far-reaching implications for political and economic stability.

Therefore, both political scientists and economists seem unable to use the standard tools provided by their disciplines to accurately predict the future. As in the case of the collapse of the Soviet Union, the circumstances that gave rise to the North African upheavals were diagnosed in great detail – but only after they had occurred. Crucially, much of this after-the-fact analysis was based on trends that were already in evidence long before the events occurred. This is significant as it suggests that the information necessary to anticipate these upheavals was in fact available but was not adequately identified, analysed and interpreted.

The butterfly effect

Why have attempts to predict major political developments been so singularly unsuccessful? Should political analysts and economists abandon all hope of gaining some insight into the future? Indeed, many economists and political analysts would argue that the future is inherently unpredictable. Thus the scenario planners WK Ralston and Ian Wilson note that mankind’s efforts at divining the future, ‘from the Delphic oracle, through augury, tarot, and the crystal ball, to the methodologies of the professional forecaster’ have all failed to ‘penetrate the veil between us and what is to come’.[4]

However, giving up entirely on gaining some insight into the future – and effectively abandoning our futures to fate – would clearly be unsatisfactory. Journalists, business leaders, politicians, military planners, and ordinary people will continue to demand futures insights from economic and political analysts. There is no choice but to persist in studying the futures of political and economic systems, and to try to understand why forecasting methods do not work.

Forecasters concentrate on identifying key current factors and trends and extrapolating them into the future in order to arrive at a single prediction or forecast at a specific point in time. The problem they run into is the extreme complexity of the current trends and events that will eventually shape the future.

Consider the huge complexity underpinning the political system in South Africa today. A plethora of actors – including organisations, businesses, political parties, courts of law, diplomats, government departments, newspapers and other media, civil society organisations, and various individuals – are constantly at work trying to change the country – and often in divergent or conflicting ways. It would be impossible to identify each of these participants and gauge the likely impact of their activities in order to understand how and why the country will change in the future.

This was a massive problem I faced when conducting research for a doctoral thesis on scenario planning at North West University (NWU). I sought to develop a method capable of predicting the long-term stability or instability of political and economic systems. However, I soon realised that the extreme complexity of these systems presented a formidable obstacle. My research supervisor, Professor André Duvenhage, and I had many conversations about this, and we eventually concluded that the complexity of political and economic systems was real, and could not be avoided. Seeking to dilute this complexity would therefore result in an artificially simplistic view of reality. By contrast, our work would need to accept the degree of complexity, and then seek to overcome its implications.

We then began to draw heavily on work done in the physical sciences in the first half of the 20th century – an era in which biologists and physicists began to grapple with the extraordinary complexity of the phenomena they were studying. One example cited in a number of academic articles was how biologists became aware of the extreme complexity that underpinned the life of a plant. They realised that it was futile to break the plant down into leaves, stalks, flowers, and roots – and even further to the atomic scale – and study each of these components in isolation, in the hope that they could then reassemble the plant and understand how it could grow. Rather, they began to understand that it was the way in which the plant interacted with its environment – air, sunlight, soil, and water – that explained its life. One could not disaggregate the plant into its component pieces; one had to study it as a whole if one wanted to understand its life and growth. Put differently, the life of a plant was far greater than the sum of its parts.

Another popular example is drawn from physics. The temperature of a gas increases because all the gas molecules rub against one another. In other words, the temperature changes because of the way in which each gas molecule interacts with all the others. Again, it would be futile to try to understand the rise in temperature by studying individual molecules in isolation. All a researcher would then see is a single molecule vibrating in a vacuum.

This work in physics and biology eventually led to the emergence, in the latter half of the 20th century, of what is known as complex systems theory. As its name suggests, it seeks to explain the behaviour of extremely complex systems that, it holds, have the following common characteristics:

 They contain very large numbers of actors or participants.

 Those actors or participants interact regularly with one another.

 Through that interaction they direct feedback into the system based on how satisfied they are with their circumstances in that system.

There are two types of feedback. The first seeks to stabilise the system and maintain its status quo. This type of feedback is directed by actors or participants who are satisfied with their circumstances. The second seeks to change the system and is directed by actors or participants whose expectations are not being met.

The two types are in constant conflict in any system. Where the former predominates the system will remain stable and will not change. However, where the latter predominates the system will change – and often dramatically.

These interactions and the feedback they produce are emergent, which means that they produce a result that is much greater than the sum of its parts.

This last idea has been popularised as the ‘butterfly effect’, namely the notion that, when a butterfly flaps its wings on one side of the world, it could cause a storm on the other side.[5]

In my experience, the best example of a complex system, and the one we use most often during our scenario briefings to introduce our audience to complexity theory, is that of traffic in an urban environment. Every day tens of thousands of motor vehicles interact with each other on roads and highways as their drivers make their way to their destinations. We all know that it takes just one actor, such as a broken-down car, or an object lying in the road, to cause a stoppage or slowdown, which rapidly builds up into a traffic jam, and eventually into a total system failure known as ‘gridlock’. The knock-on effects are enormous, ranging from the human and material costs of accidents to huge sums in lost economic productivity. It would be futile to study each vehicle in a traffic jam to understand its cause. A traffic jam can only be explained by examining how the vehicles in question interacted with one another. Therefore, major traffic jams and their consequences are a perfect example of the emergent property of complex systems – or the butterfly effect at work.

At North West University, we developed the proposition that similar effects can be observed in any political or economic system. A small and seemingly insignificant change in the behaviour of just one actor in such a system can affect all the other actors in that system, and eventually change its future. We produced the following very simple equations to demonstrate this effect as it might play itself out in a political system:

Imagine that there are only three actors, or participants, in the South African economy, and assume that each contributes a value of 2 to the economy. If the economy was a simple system, in which participants made these contributions in isolation of one another, the system could be expressed by adding their contributions together, as follows:

2 + 2 + 2 = 6

However, in the case of a more complex system, the individual contributions or components would need to be multiplied, as follows:

2 x 2 x 2 = 8

Now consider what happens if at some point in the future one of the actors contributes a 1 and not a 2 to the system. The simple system would now look like this:

2 + 2 + 1 = 5

Here the outcome for the system has changed from a value of 6 to a 5. Had a forecast been made of the future of such a system, and the forecaster had been unaware that one of the actors in the system would no longer contribute a 2 to the system, the forecast would still have been reasonably accurate. However, the complex system would change radically, as follows:

2 x 2 x 1 = 4

Here, the outcome for the system has changed from an 8 to a 4. This time, had a forecast been made of the future of this system, and the forecaster been unaware that one of the actors would no longer contribute a 2, the forecast would have been totally wrong.

These calculations are perfect illustrations of the butterfly effect – in other words, that a small, seemingly inconsequential, change in the behaviour of even one actor in a system of millions of actors could dramatically change the future of that system. This is why no one can forecast what the traffic will be like at any given point in the future. The fact that the actions of just one vehicle or driver can completely thwart the plans of all other drivers makes such a forecast impossible.

John Kane-Berman is fond of telling a story about the former National Party and later Herstigte Nasionale Party politician Jaap Marais that effectively demonstrates the quality of emergence, or the butterfly effect, in a political context. In 1968 the then prime minister, John Vorster, agreed that the Springboks could play an All Black team that included Maoris. Marais warned him that such a compromise would one day cause ‘a black man to marry your daughter, and sit next to you in parliament’. His NP colleagues laughed this off, but Marais was actually right: in compromising once on the principle of racial separation, Vorster unintentionally contributed to a chain of events that would culminate in the ending of apartheid.

No forecaster could possibly have realised what the consequences of Vorster’s decision would be. Consider the thousands of significant decisions taken every year over the life of the apartheid system by a huge range of role players including the NP government, political movements in exile, internal resistance movements, civil society organisations, foreign governments and other international institutions, and many others, and one starts to realise why accurately predicting the future at that point was effectively impossible.

What our research at NWU revealed is that, in the case of complex systems such as political systems, the future can never be accurately forecast to a single point in time and space. Such forecasts are largely based on known trends, which are then projected into the future,[6] but even a seemingly insignificant shift in one of those trends can completely change its future trajectory.

We concluded that all the past failures of political and economic forecasting resulted from this problem.[7] It is not possible for any analyst, no matter how well-informed, to track every single shift in South Africa’s current political system – and therefore equally impossible for any forecaster to extrapolate current trends into a single accurate prediction of the future. For this reason we need to do what many scenario thinkers have done before, and that is to move away from the idea that a single pre-ordained future exists and rather reassess our view of the future and how we think about it.

Four types of futures

In his excellent book 20/20 Foresight: Crafting Strategy in an Uncertain World, the futurist Hugh Courtney comes up with the radical and brilliant idea that there are different variants or types of futures that are distinguished from one another by their relative uncertainty. He cites four such types, namely:

 A ‘clear enough future’, when the range of possible outcomes is so narrow that uncertainty does not matter. This does not imply that such a future is perfectly predictable, but rather that it is predictable enough for a dominant strategy of choice to suit all plausible outcomes.

 An ‘alternate future’, when it is possible to identify a limited set of possible outcomes. In this instance, Courtney cites potential legislative or judicial changes as examples.

 A ‘range of futures’, when the future is more uncertain than an ‘alternate future’, as it is not possible to identify a shortlist of possible outcomes. Rather, it is necessary to develop a broad range of plausible outcomes. Courtney cites unstable macro-economic conditions as an example of such a future.

 A ‘truly ambiguous future’, when it may be difficult even to identify a range of possible outcomes. The impact of major economic or social discontinuities is an example of this kind of future.[8]

Courtney’s analysis is of great value since it shows us that there is not just one kind of future that applies to all economies and all countries. Instead, we should rather categorise the different variants or kinds of futures according to their relative uncertainty. This will help us navigate these futures. Most importantly, and this really is something to get your head around, in very uncertain environments a number of different futures may be possible for the same country.

While Courtney notes that some types of futures are more uncertain than others, Ralston and Wilson warn that the future of most systems is becoming increasingly uncertain all the time. This, they argue, is due to the concept of ‘change in the character of change itself’ – another idea that requires some thinking. Here they cite the growing number of people working to change the world, especially in areas of technological innovation, and the ‘radical compression’ of the time taken for these innovations to be developed. They assert that innovations in areas of information technology, nuclear power, biomedical advances, and nanotechnology ‘will have a more pervasive influence on human life than any other previous technologies in human history’.[9]

Hence futures researchers need to be aware not only that some futures are more uncertain than others, but also that many futures are becoming increasingly uncertain all the time. Which type or variant of the future is most relevant to a particular analyst will therefore depend on what aspect of a system is being studied, and within what time frame this is being done.

For example, if a corporation commissioned us to determine whether the ANC would still be in power after the 2014 elections, this would suggest a ‘clear enough’ or ‘alternate’ future, as a range of possible outcomes could be listed. These would include the ANC losing the election, being driven from power by popular protests, being eroded by internal conflict, and so on. The point is that it would be possible to list a short number of possible outcomes, and to be sure that one of these will come to pass.

However, as time frames lengthen, the emergent character (or butterfly effect) of South Africa’s political system will probably mean that the type of future will become the third or even the fourth variant where we would have to suggest a number of different plausible outcomes for the country and not just a single forecast of a single future.

The advent of scenario planning

There is, of course, a considerable difference between predicting a single future and developing a series of plausible ones. As I have shown, the method of forecasting – in seeking to identify a single most probable outcome – is a risky method for determining South Africa’s political future. The uncertainty inherent in the butterfly effect is simply too great for such a forecast to succeed other than by sheer luck.

Yet many institutions and other role players still demand or expect these sorts of forecasts. The continued use of this approach explains why political scientists and economists struggle to accurately predict the future of economies and countries. It therefore appears that a more effective method is required if we are to understand what South Africa’s future holds.

Pierre Wack, perhaps the world’s most respected scenario planner, and the forefather of much current futures research, made exactly this point almost 30 years ago when he wrote that, despite the obvious failures of forecasting, many corporations continue to do so ‘because no one has developed a better way of dealing with economic uncertainty’.[10] According to Wack the answer to poor forecasting is not to hire better forecasters. He argued that the future is too uncertain for any forecasting technique to be effective, and that it is necessary to develop new and better methods – namely scenario methods – for studying the future.

There is a fascinating history behind the scenario methods Wack was referring to, and understanding this history is essential to understanding exactly why these methods are so effective. A number of great books have been written on the subject but what follows is an abridged history.

The methodology of scenario planning developed in response to the great uncertainties presented by the advent of the Cold War when two superpowers – the Soviet Union and the United States – confronted each other in a binary nuclear standoff. Strategic planning failures in such an environment could obviously have had catastrophic consequences. Just think of the implications of either party developing an entirely inaccurate perception of the other’s intentions, while both have their finger on the nuclear button.

In this tense environment, the Rand Corporation was established in order to conduct research into new weapons systems, particularly for the United States Air Force.[11] A researcher at the Rand Corporation, Hermann Kahn, developed a new method for gaining insight into the future by developing descriptions of a series of alternative futures – written in the present tense, as if they had already come to pass.[12] He called them ‘scenarios’, the movie term for a detailed screenplay.[13] The Californian thrash metal band Megadeth takes its name from one of these scenarios, which describes how one million lives are lost in a nuclear blast.[14]

Kahn went on to found the Hudson Institute in the 1960s, which produced a number of Cold War scenarios, centred on alternative accounts of nuclear conflict.[15] The Hudson Institute began to attract sponsorship from large corporations such as General Motors, IBM, and Royal Dutch Shell,[16] which led to them being exposed to early scenario thinking. General Electric, for example, decided to include scenarios in its approach to strategic planning.

However, the role and status of scenario development as a tool for gaining greater insight into the future was only consolidated by a series of events that started in the planning department of Royal Dutch Shell in 1967, and culminated in the 1973 Yom Kippur War. By the mid-1960s, Royal Dutch Shell had realised that extreme long-term planning – beyond that provided by traditional planning and forecasting methods – was necessary in the oil industry. [17] As a result, it began to look ahead to the likely business environment in the year 2000.[18] According to Wack[19] – who led Shell’s planning efforts – and Gill Ringland,[20] this study indicated that ‘the predictable, surprise-free [oil industry] environment would not continue’, and identified the possibility of a major shift in the balance of power between oil companies and oil producers in favour of the latter that would significantly increase the price of oil. At the same time the Organisation of Petroleum Exporting Countries (OPEC) had begun to ‘[flex] its political muscles’.[21]

The conclusions of the Shell study were significant, as both oil prices and supply had seemed predictable since the mid-1940s.[22] In response to this newly identified uncertainty, Shell broke with traditional forecasting practice and produced two detailed scenarios of the global oil system.[23] One suggested that the oil price would remain stable, which was the globally accepted opinion at that time. The other suggested that the oil price would rise significantly. Shell realised that the Arab oil-producing countries could and would demand higher oil prices. The question that could not be answered was when this would happen.[24]

The oil industry is obviously a complex system. Scores of producer nations supply scores of consumer nations, who in turn supply billions of oil users and hundreds and thousands of businesses that depend directly and indirectly on the supply and price of oil. In the event, the impetus for the increase in the oil price was provided in 1973 by the Yom Kippur War between Israel and its Middle Eastern neighbours.[25] Arab nations resented Western support for Israel. Within weeks, ordinary motorists around the world faced steeply rising petrol prices.

This changed the balance of power between mainly Western oil companies and their mainly Middle Eastern suppliers just as Shell had suggested (and its directors had been alone in planning for).[26] This could not have been achieved through traditional methods of forecasting. As a result, it was better placed than other global oil companies to respond to the price increases, which allowed it to assume a leading role in the global oil business.[27]

Following the shock of events round the Yom Kippur War, and growing awareness of Shell’s pioneering approach, a significant number of Fortune 100 companies went on to adopt scenario-based planning.[28] A survey of 200 large corporations soon identified scenario-based planning as the most common strategic planning tool.[29]

A growing appreciation of the potential consequences of strategic errors in the Cold War context therefore forced some analysts to adopt a new approach to studying the future. This method caused them to dispense with the notion that there was one pre-ordained future, and that capable forecasters could accurately identify that future. Instead, they abandoned the certainty promised by traditional forecasting methods in favour of identifying a range of equally plausible futures for the same system or country. This is what later enabled Shell’s scenario planners to identify the possibility that Gorbachev could bring about major political and economic reforms in Russia.

Professor Duvenhage and I immediately understood that the strength, and later success, of this method could be ascribed to a single characteristic, namely its inherent ability to overcome the butterfly effect. For this reason scenario planning dovetailed very neatly with our work on complex systems and freed us from guessing at the future. In scenario planning we therefore found a methodology we could apply to our work on complex systems theory to determine the prospects for South Africa’s long-term stability. The final conclusions of that study are the focus of this book.

The failure of analysts and economists to anticipate events such as the Arab Spring and the global financial crisis therefore suggests two things. The first is that the butterfly effect means that the future of complex systems is plural rather than singular. The second is that scenario building is the only method that can overcome the consequences of the butterfly effect.

Therefore, in the case of South Africa, any serious attempt to gain greater insight into our future must be based on a series of equally plausible scenarios, plus – and this will become very important in this book – a method for navigating our way through time towards the scenario that will eventually materialise.